In order to observe the effect of the network architecture on the proposed approach, we deploy two architectures, namely Inception-BN [1]} and ResNet-50 [2]} as the feature extractors, pre-trained on the ImageNet ILSVRC dataset. In both the cases, the embeddings are l\(_2\) -normalized and the batch normalization layers are frozen. The learning rate is set as \(10^{-5}\) , and a weight decay multiplier of \(4\times 10^{-4}\)  is used. The rest of the parameters are kept the same as mentioned in the paper. We report the NMI and Recall@K values to measure the performance.
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